Overview

Dataset statistics

Number of variables10
Number of observations836
Missing cells313
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory65.4 KiB
Average record size in memory80.2 B

Variable types

Numeric9
Categorical1

Alerts

Brand me has a high cardinality: 427 distinct values High cardinality
Ratings is highly correlated with Mobile_Size and 2 other fieldsHigh correlation
ROM is highly correlated with Mobile_Size and 1 other fieldsHigh correlation
Mobile_Size is highly correlated with Ratings and 3 other fieldsHigh correlation
Battery_Power is highly correlated with Ratings and 2 other fieldsHigh correlation
Price is highly correlated with Ratings and 3 other fieldsHigh correlation
Ratings is highly correlated with Mobile_Size and 1 other fieldsHigh correlation
Mobile_Size is highly correlated with Ratings and 1 other fieldsHigh correlation
Price is highly correlated with Ratings and 1 other fieldsHigh correlation
Ratings is highly correlated with ROM and 4 other fieldsHigh correlation
ROM is highly correlated with Ratings and 2 other fieldsHigh correlation
Mobile_Size is highly correlated with Ratings and 2 other fieldsHigh correlation
Primary_Cam is highly correlated with RatingsHigh correlation
Battery_Power is highly correlated with Ratings and 2 other fieldsHigh correlation
Price is highly correlated with RatingsHigh correlation
Ratings has 31 (3.7%) missing values Missing
Selfi_Cam has 269 (32.2%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique

Reproduction

Analysis started2022-02-17 02:31:46.357099
Analysis finished2022-02-17 02:32:00.440622
Duration14.08 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct836
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean417.5
Minimum0
Maximum835
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-02-17T02:32:00.541393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.75
Q1208.75
median417.5
Q3626.25
95-th percentile793.25
Maximum835
Range835
Interquartile range (IQR)417.5

Descriptive statistics

Standard deviation241.476707
Coefficient of variation (CV)0.578387322
Kurtosis-1.2
Mean417.5
Median Absolute Deviation (MAD)209
Skewness0
Sum349030
Variance58311
MonotonicityStrictly increasing
2022-02-17T02:32:00.739017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8351
 
0.1%
3121
 
0.1%
2841
 
0.1%
2831
 
0.1%
2821
 
0.1%
2811
 
0.1%
2801
 
0.1%
2791
 
0.1%
2781
 
0.1%
2771
 
0.1%
Other values (826)826
98.8%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
8351
0.1%
8341
0.1%
8331
0.1%
8321
0.1%
8311
0.1%
8301
0.1%
8291
0.1%
8281
0.1%
8271
0.1%
8261
0.1%

Brand me
Categorical

HIGH CARDINALITY

Distinct427
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
Kechaoda A27
 
11
Jivi R21Plus
 
8
Easyfone Star
 
8
Lava 34
 
8
MTR Ferrari
 
7
Other values (422)
794 

Length

Max length45
Median length17
Mean length20.89593301
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique212 ?
Unique (%)25.4%

Sample

1st rowLG V30+ (Black, 128 )
2nd rowI Kall K11
3rd rowNokia 105 ss
4th rowSamsung Galaxy A50 (White, 64 )
5th rowPOCO F1 (Steel Blue, 128 )

Common Values

ValueCountFrequency (%)
Kechaoda A2711
 
1.3%
Jivi R21Plus8
 
1.0%
Easyfone Star8
 
1.0%
Lava 34 8
 
1.0%
MTR Ferrari7
 
0.8%
Nokia 3310 DS6
 
0.7%
I Kall K116
 
0.7%
Nokia 1056
 
0.7%
Snexian Guru 3326
 
0.7%
Inovu A96
 
0.7%
Other values (417)764
91.4%

Length

2022-02-17T02:32:00.958618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
359
 
9.8%
128135
 
3.7%
black111
 
3.0%
6495
 
2.6%
pro82
 
2.2%
blue72
 
2.0%
samsung70
 
1.9%
i62
 
1.7%
kall61
 
1.7%
galaxy60
 
1.6%
Other values (494)2551
69.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Ratings
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct20
Distinct (%)2.5%
Missing31
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean4.10310559
Minimum2.8
Maximum4.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-02-17T02:32:01.302026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile3.5
Q13.8
median4.1
Q34.4
95-th percentile4.6
Maximum4.8
Range2
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.3653564947
Coefficient of variation (CV)0.08904389289
Kurtosis-0.2700665768
Mean4.10310559
Median Absolute Deviation (MAD)0.3
Skewness-0.5191798924
Sum3303
Variance0.1334853682
MonotonicityNot monotonic
2022-02-17T02:32:01.468549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4.5109
13.0%
4.197
11.6%
4.491
10.9%
3.974
8.9%
4.372
8.6%
3.863
7.5%
4.258
6.9%
443
 
5.1%
3.736
 
4.3%
3.634
 
4.1%
Other values (10)128
15.3%
ValueCountFrequency (%)
2.82
 
0.2%
34
 
0.5%
3.11
 
0.1%
3.23
 
0.4%
3.32
 
0.2%
3.427
3.2%
3.532
3.8%
3.634
4.1%
3.736
4.3%
3.863
7.5%
ValueCountFrequency (%)
4.81
 
0.1%
4.725
 
3.0%
4.631
 
3.7%
4.5109
13.0%
4.491
10.9%
4.372
8.6%
4.258
6.9%
4.197
11.6%
443
 
5.1%
3.974
8.9%

RAM
Real number (ℝ≥0)

Distinct13
Distinct (%)1.6%
Missing7
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean6.066344994
Minimum0
Maximum34
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-02-17T02:32:01.591176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median6
Q36
95-th percentile8
Maximum34
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.530335523
Coefficient of variation (CV)0.4171103894
Kurtosis30.67203564
Mean6.066344994
Median Absolute Deviation (MAD)0
Skewness3.267384882
Sum5029
Variance6.402597857
MonotonicityNot monotonic
2022-02-17T02:32:01.694201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
6430
51.4%
8158
 
18.9%
4113
 
13.5%
340
 
4.8%
1231
 
3.7%
221
 
2.5%
120
 
2.4%
107
 
0.8%
54
 
0.5%
02
 
0.2%
Other values (3)3
 
0.4%
(Missing)7
 
0.8%
ValueCountFrequency (%)
02
 
0.2%
120
 
2.4%
221
 
2.5%
340
 
4.8%
4113
 
13.5%
54
 
0.5%
6430
51.4%
8158
 
18.9%
107
 
0.8%
1231
 
3.7%
ValueCountFrequency (%)
341
 
0.1%
301
 
0.1%
251
 
0.1%
1231
 
3.7%
107
 
0.8%
8158
 
18.9%
6430
51.4%
54
 
0.5%
4113
 
13.5%
340
 
4.8%

ROM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct22
Distinct (%)2.6%
Missing4
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean64.37307692
Minimum0
Maximum256
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-02-17T02:32:01.809777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q132
median40
Q364
95-th percentile128
Maximum256
Range256
Interquartile range (IQR)32

Descriptive statistics

Standard deviation53.44782488
Coefficient of variation (CV)0.8302822769
Kurtosis4.103707674
Mean64.37307692
Median Absolute Deviation (MAD)24
Skewness1.932462432
Sum53558.4
Variance2856.669984
MonotonicityNot monotonic
2022-02-17T02:32:01.935206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
32316
37.8%
64236
28.2%
128142
17.0%
25633
 
3.9%
1629
 
3.5%
425
 
3.0%
2414
 
1.7%
2510
 
1.2%
33
 
0.4%
403
 
0.4%
Other values (12)21
 
2.5%
(Missing)4
 
0.5%
ValueCountFrequency (%)
01
 
0.1%
21
 
0.1%
2.41
 
0.1%
33
 
0.4%
425
3.0%
83
 
0.4%
101
 
0.1%
123
 
0.4%
151
 
0.1%
1629
3.5%
ValueCountFrequency (%)
25633
 
3.9%
128142
17.0%
64236
28.2%
562
 
0.2%
511
 
0.1%
403
 
0.4%
353
 
0.4%
32316
37.8%
2510
 
1.2%
2414
 
1.7%

Mobile_Size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct47
Distinct (%)5.6%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.597281775
Minimum2
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-02-17T02:32:02.076000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.4
Q14.5
median4.77
Q36.3
95-th percentile6.67
Maximum44
Range42
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation3.89866446
Coefficient of variation (CV)0.6965281751
Kurtosis88.06812246
Mean5.597281775
Median Absolute Deviation (MAD)0.27
Skewness9.182997701
Sum4668.133
Variance15.19958457
MonotonicityNot monotonic
2022-02-17T02:32:02.252346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
4.5189
22.6%
4.54143
17.1%
4.7757
 
6.8%
6.537
 
4.4%
6.728
 
3.3%
6.424
 
2.9%
4.423
 
2.8%
6.223
 
2.8%
5.519
 
2.3%
6.4419
 
2.3%
Other values (37)272
32.5%
ValueCountFrequency (%)
29
 
1.1%
3.715
 
1.8%
4.423
 
2.8%
4.5189
22.6%
4.5031
 
0.1%
4.523
 
0.4%
4.54143
17.1%
4.572
 
0.2%
4.5813
 
1.6%
4.718
 
2.2%
ValueCountFrequency (%)
448
 
1.0%
72
 
0.2%
6.728
3.3%
6.6713
 
1.6%
6.64
 
0.5%
6.594
 
0.5%
6.554
 
0.5%
6.5311
 
1.3%
6.522
 
0.2%
6.537
4.4%

Primary_Cam
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.98325359
Minimum5
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-02-17T02:32:02.422817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q148
median48
Q348
95-th percentile64
Maximum64
Range59
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.17009296
Coefficient of variation (CV)0.2327914871
Kurtosis1.506861361
Mean47.98325359
Median Absolute Deviation (MAD)0
Skewness-0.6192262891
Sum40114
Variance124.7709767
MonotonicityNot monotonic
2022-02-17T02:32:02.566285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
48458
54.8%
64183
 
21.9%
3583
 
9.9%
3872
 
8.6%
2511
 
1.3%
89
 
1.1%
166
 
0.7%
205
 
0.6%
264
 
0.5%
403
 
0.4%
ValueCountFrequency (%)
52
 
0.2%
89
 
1.1%
166
 
0.7%
205
 
0.6%
2511
 
1.3%
264
 
0.5%
3583
 
9.9%
3872
 
8.6%
403
 
0.4%
48458
54.8%
ValueCountFrequency (%)
64183
 
21.9%
48458
54.8%
403
 
0.4%
3872
 
8.6%
3583
 
9.9%
264
 
0.5%
2511
 
1.3%
205
 
0.6%
166
 
0.7%
89
 
1.1%

Selfi_Cam
Real number (ℝ≥0)

MISSING

Distinct23
Distinct (%)4.1%
Missing269
Missing (%)32.2%
Infinite0
Infinite (%)0.0%
Mean9.784832451
Minimum0
Maximum61
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-02-17T02:32:02.725863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q313
95-th percentile20
Maximum61
Range61
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.503838165
Coefficient of variation (CV)0.6646856957
Kurtosis7.3009124
Mean9.784832451
Median Absolute Deviation (MAD)4
Skewness1.621974765
Sum5548
Variance42.29991088
MonotonicityNot monotonic
2022-02-17T02:32:02.891875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8114
13.6%
5100
 
12.0%
1280
 
9.6%
1366
 
7.9%
260
 
7.2%
2031
 
3.7%
121
 
2.5%
1520
 
2.4%
329
 
1.1%
169
 
1.1%
Other values (13)57
 
6.8%
(Missing)269
32.2%
ValueCountFrequency (%)
05
 
0.6%
121
 
2.5%
260
7.2%
41
 
0.1%
5100
12.0%
61
 
0.1%
79
 
1.1%
8114
13.6%
102
 
0.2%
118
 
1.0%
ValueCountFrequency (%)
611
 
0.1%
329
 
1.1%
237
 
0.8%
227
 
0.8%
213
 
0.4%
2031
3.7%
185
 
0.6%
171
 
0.1%
169
 
1.1%
1520
2.4%

Battery_Power
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3274.688995
Minimum1020
Maximum6000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-02-17T02:32:03.081926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1020
5-th percentile1200
Q13000
median3000
Q33800
95-th percentile4720
Maximum6000
Range4980
Interquartile range (IQR)800

Descriptive statistics

Standard deviation927.5188518
Coefficient of variation (CV)0.2832387604
Kurtosis0.5782028642
Mean3274.688995
Median Absolute Deviation (MAD)500
Skewness-0.2445574256
Sum2737640
Variance860291.2205
MonotonicityNot monotonic
2022-02-17T02:32:03.258104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3000272
32.5%
3500123
14.7%
3800100
 
12.0%
470048
 
5.7%
250038
 
4.5%
500033
 
3.9%
175030
 
3.6%
105019
 
2.3%
280019
 
2.3%
120017
 
2.0%
Other values (39)137
16.4%
ValueCountFrequency (%)
10204
 
0.5%
105019
2.3%
10804
 
0.5%
11001
 
0.1%
120017
2.0%
15003
 
0.4%
15507
 
0.8%
175030
3.6%
19005
 
0.6%
20007
 
0.8%
ValueCountFrequency (%)
60006
 
0.7%
500033
3.9%
49001
 
0.1%
47802
 
0.2%
470048
5.7%
46001
 
0.1%
45501
 
0.1%
44408
 
1.0%
43001
 
0.1%
423011
 
1.3%

Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct253
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18220.34689
Minimum479
Maximum573000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-02-17T02:32:03.453068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum479
5-th percentile649
Q1984.75
median1697
Q318999
95-th percentile56999
Maximum573000
Range572521
Interquartile range (IQR)18014.25

Descriptive statistics

Standard deviation52805.55022
Coefficient of variation (CV)2.898163824
Kurtosis83.99596667
Mean18220.34689
Median Absolute Deviation (MAD)1055
Skewness8.505928436
Sum15232210
Variance2788426134
MonotonicityNot monotonic
2022-02-17T02:32:03.646152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64932
 
3.8%
119919
 
2.3%
79919
 
2.3%
109917
 
2.0%
129915
 
1.8%
2499914
 
1.7%
134914
 
1.7%
59912
 
1.4%
74911
 
1.3%
1899911
 
1.3%
Other values (243)672
80.4%
ValueCountFrequency (%)
4791
 
0.1%
5391
 
0.1%
5591
 
0.1%
5951
 
0.1%
59912
 
1.4%
6296
 
0.7%
63910
 
1.2%
6451
 
0.1%
64932
3.8%
6502
 
0.2%
ValueCountFrequency (%)
5730004
0.5%
5630002
 
0.2%
2530001
 
0.1%
1530001
 
0.1%
1403005
0.6%
1213002
 
0.2%
1171006
0.7%
1124501
 
0.1%
1066002
 
0.2%
1030001
 
0.1%

Interactions

2022-02-17T02:31:58.137634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T02:31:47.748682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T02:31:48.985461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T02:31:50.325605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T02:31:51.628394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T02:31:53.071765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T02:31:54.301481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T02:31:55.540760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T02:31:56.888259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T02:31:58.283942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T02:31:47.875866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-17T02:32:04.011165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-17T02:32:04.196356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-17T02:32:04.532323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-17T02:31:59.522403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-17T02:31:59.869844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-17T02:32:00.152715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-17T02:32:00.285322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0Brand meRatingsRAMROMMobile_SizePrimary_CamSelfi_CamBattery_PowerPrice
00LG V30+ (Black, 128 )4.34.0128.06.004813.0400024999
11I Kall K113.46.064.04.504812.0400015999
22Nokia 105 ss4.34.04.04.506416.0400015000
33Samsung Galaxy A50 (White, 64 )4.46.064.06.404815.0380018999
44POCO F1 (Steel Blue, 128 )4.56.0128.06.183515.0380018999
55Apple iPhone 11 Pro (Space Grey, 512 )4.78.0128.05.803512.05000140300
66Samsung Galaxy A70s (Prism Crush Red, 128 )4.48.0128.06.70645.0470029999
77Samsung Galaxy S10 Lite (Prism Blue, 512 )4.58.0128.06.704812.0470047999
88OPPO A9 (Marble Green, 128 )4.44.0128.06.53482.0402016490
99POCO F1 (Graphite Black, 256 )4.58.0256.06.18355.0380022999

Last rows

Unnamed: 0Brand meRatingsRAMROMMobile_SizePrimary_CamSelfi_CamBattery_PowerPrice
826826Easyfone Amico3.56.032.04.5248NaN35002599
827827Apple iPhone 11 Pro Max (Gold, 64 )4.78.064.06.503512.03500117100
828828Samsung Galaxy S8 Plus (Midnight Black, 64 )4.64.064.06.20358.0350053990
829829Black Shark 2 (Shadow Black, 128 )4.46.0128.06.394812.0380031999
830830InFocus Vibe 13.66.032.04.5048NaN3000898
831831Karbonn K24 Plus Pro3.86.032.04.544812.028001299
832832InFocus POWER 24.18.064.04.5464NaN25001390
833833Alcatel 5V (Spectrum Blue, 32 )4.43.032.06.20481.038009790
834834JIVI JV 12M3.710.032.04.5064NaN3500799
835835Blacear B5 Grip3.56.032.04.506415.01050799